论文标题
基于Kappa和F-Score的选择性聚类合奏
Selective clustering ensemble based on kappa and F-score
论文作者
论文摘要
聚类合奏在提高分区结果的准确性和鲁棒性方面具有令人印象深刻的表现，并且近年来受到了很多关注。选择性聚类集合（SCE）可以通过根据多样性和稳定性选择基本分区或集群来进一步提高集合性能。但是，多样性和稳定之间存在冲突，以及如何在两者之间进行权衡是具有挑战性的。这里的关键是如何评估基本分区和集群的质量。在本文中，我们提出了一种使用KAPPA和F-SCORE的分区和集群的新评估方法，从而导致了一种新的SCE方法，该方法使用KAPPA选择信息性的基本分区，并使用F-SCORE根据稳定性来量群。该方法的有效性和效率通过实际数据集经验验证。
Clustering ensemble has an impressive performance in improving the accuracy and robustness of partition results and has received much attention in recent years. Selective clustering ensemble (SCE) can further improve the ensemble performance by selecting base partitions or clusters in according to diversity and stability. However, there is a conflict between diversity and stability, and how to make the trade-off between the two is challenging. The key here is how to evaluate the quality of the base partitions and clusters. In this paper, we propose a new evaluation method for partitions and clusters using kappa and F-score, leading to a new SCE method, which uses kappa to select informative base partitions and uses F-score to weight clusters based on stability. The effectiveness and efficiency of the proposed method is empirically validated over real datasets.
